With the increasing use of computers and computerized technology, the amount of information represented digitally has become enormous. Analysis of these vast quantities of digital data generally involves the recognition of known patterns.
In many cases, information that originates in a digital form is ultimately analyzed through manual review by a person, often requiring substantial training. For example, medical image analysis typically requires a high level of expertise. In order for people to interact with the volumes of digital data, the information is typically converted into a visual, audible, or other human-perceivable representation. However, during the process of translating digital data from its raw form into a convenient output form, some information can be lost. Data is often processed and filtered for presentation before analysis, losing significant information from the original data. For example, the data of ultrasound, seismic, and sonar signals are all initially based on sound. The data of each of these is typically processed into a graphical form for display, but the processing often sacrifices substantial meaning and detail for the sake of human readability.
While humans can be trained to analyze many different types of data, manual human analysis is generally more expensive than automated systems. Additionally, errors are often introduced due to the limits of human perception and attention span. The data often contains more detail than human senses can discern, and it is well-known that repetition causes errors.
To address these shortcomings of human analysis, many automated pattern recognition systems have been developed. However, most of these solutions are highly data-specific. The inputs that a pattern recognition system can handle are often fixed and limited by design. Many systems are inherently limited by design on the basis that many systems are designed by use on a specific modality. For example, medical image analysis systems perform well on X-ray or MR imagery but perform poorly on seismic data. The reverse is also true. The system by which the data is evaluated is tightly coupled with the specific data source it was designed to evaluate. Therefore, improvements across a broad range of systems are very difficult.
Within each system, pattern and feature recognition is processing-intensive. For example, image analysis commonly uses complex algorithms to find shapes, requiring thousands of algorithms to be processed. The time to discover, develop, and implement each algorithm causes an incremental delay in deploying or improving the system.
Thus, there still remains substantial room for improvement in the field of automated pattern recognition systems.
Additionally, most experts predict that under conventional practices, avian influenza will not be able to be detected rapidly enough to prevent a flu pandemic that could hit the world within the next few years. Currently, there is not a rapid screening method for birds, other animals, or humans. In the event of a pandemic, current methods would require vast amounts of pathology skilled manpower to examine blood or other fluid samples in order to detect and track the Avian Flu. The amount of pathology skilled manpower could not be attained.
Therefore there is a need for an automatic avian influenza virus detection system and method.